Given the accuracy model and the block library, the NSGA-II [1]}, [2]} evolutionary algorithm is executed to find Pareto-optimal architectures that maximize model accuracy and minimize a target cost function, see Figure REF (c). The cost function can be scenario-agnostic, such as the number of operations or the number of parameters in the network, or scenario-aware, such as on-device latency, throughput, or energy. In this work, full-network latency is considered as a cost function by using direct hardware measurements in the optimization loop. At the end of this process, the Pareto-optimal models yielded by the NSGA-II are finetuned to obtain the final models (Section REF ).
